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We report on the First International Competition on Computational Models of Argumentation (ICCMA'15) which took place in the first half of 2015 and focused on reasoning tasks in abstract argumentation frameworks. Performance of submitted solvers was evaluated on four computational problems wrt. four different semantics relating to the verification of the acceptance status of arguments, and computing jointly acceptable sets of arguments. In this paper, we describe the technical setup of the competition, and give an overview on the submitted solvers. Moreover, we report on the results and discuss our findings. [ABSTRACT FROM AUTHOR]

Abstract: MinSAT is the problem of finding a truth assignment that minimizes the number of satisfied clauses in a CNF formula. When we distinguish between hard and soft clauses, and soft clauses have an associated weight, then the problem, called Weighted Partial MinSAT, consists in finding a truth assignment that satisfies all the hard clauses and minimizes the sum of weights of satisfied soft clauses. In this paper we describe a branch-and-bound solver for Weighted Partial MinSAT equipped with original upper bounds that exploit both clique partitioning algorithms and MaxSAT technology. Then, we report on an empirical investigation that shows that solving combinatorial optimization problems by reducing them to MinSAT is a competitive generic problem solving approach when solving MaxClique and combinatorial auction instances. Finally, we investigate an interesting correlation between the minimum number and the maximum number of satisfied clauses on random CNF formulae. [Copyright &y& Elsevier]

Abstract: In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. [Copyright &y& Elsevier]

Abstract: The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especially for mining itemsets. The approach taken in the field of data mining contrasts with the constraint programming principles developed within the artificial intelligence community. While most data mining research focuses on algorithmic issues and aims at developing highly optimized and scalable implementations that are tailored towards specific tasks, constraint programming employs a more declarative approach. The emphasis lies on developing high-level modeling languages and general solvers that specify what the problem is, rather than outlining how a solution should be computed, yet are powerful enough to be used across a wide variety of applications and application domains. This paper contributes a declarative constraint programming approach to data mining. More specifically, we show that it is possible to employ off-the-shelf constraint programming techniques for modeling and solving a wide variety of constraint-based itemset mining tasks, such as frequent, closed, discriminative, and cost-based itemset mining. In particular, we develop a basic constraint programming model for specifying frequent itemsets and show that this model can easily be extended to realize the other settings. This contrasts with typical procedural data mining systems where the underlying procedures need to be modified in order to accommodate new types of constraint, or novel combinations thereof. Even though the performance of state-of-the-art data mining systems outperforms that of the constraint programming approach on some standard tasks, we also show that there exist problems where the constraint programming approach leads to significant performance improvements over state-of-the-art methods in data mining and as well as to new insights into the underlying data mining problems. Many such insights can be obtained by relating the underlying search algorithms of data mining and constraint programming systems to one another. We discuss a number of interesting new research questions and challenges raised by the declarative constraint programming approach to data mining. [Copyright &y& Elsevier]

Abstract: AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input. However, building action models from scratch is a difficult and time-consuming task, even for experts. In this paper, we develop an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of observed plans. In fact, our approach works when no or partial intermediate states are given. These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically. [Copyright &y& Elsevier]

Abstract: The role of argumentation in supporting various forms of interaction among possibly conflicting autonomous agents has been explicitly recognized in the literature. In argumentation, conflict management is carried out by the formal process of defeat status computation. In this paper we consider the generalization of this process to a distributed setting. We show that significant stabilization problems may arise even in relatively simple cases. A fundamental negative result is then proved: no general self-stabilizing algorithm exists for distributed defeat status computation, indicating that self-stabilizing algorithms for this problem can be defined only under specific conditions. Accordingly, we focus on two cases: an algorithm tailored to a specific family of inference graphs, that include only rebutting defeaters, and an algorithm that applies to any inference graph, also including undercutting defeaters, but may provide (cautiously) incorrect results for some nodes. For both algorithms the worst-case round complexity is analyzed and it is proved that no algorithms with lower complexity exist for the same tasks. [Copyright &y& Elsevier]